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Plotting Multiple Lines on a Graph in R: A Step-by-Step Guide

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< section id="introduction" class="level1">

Introduction

Graphs are powerful visual tools for analyzing and presenting data. In this blog post, we will explore how to plot multiple lines on a graph using base R. We will cover two methods: matplot() and lines(). These functions provide flexibility and control over the appearance of the lines, allowing you to create informative and visually appealing plots. So, let’s dive in and learn how to plot multiple lines on a graph in R!

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Examples

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Example 1 Using matplot():

The matplot() function is a convenient way to plot multiple lines in one chart when you have a dataset in a wide format. Here’s an example:

# Create sample data
x <- 1:10
y1 <- c(1, 4, 3, 6, 5, 8, 7, 9, 10, 2)
y2 <- c(2, 5, 4, 7, 6, 9, 8, 10, 3, 1)
y3 <- c(3, 6, 5, 8, 7, 10, 9, 2, 4, 1)

# Plot multiple lines using matplot
matplot(x, cbind(y1, y2, y3), type = "l", lty = 1, 
        col = c("red", "blue", "green"), xlab = "X", 
        ylab = "Y", main = "Multiple Lines Plot")
legend("topright", legend = c("Line 1", "Line 2", "Line 3"), 
       col = c("red", "blue", "green"), 
       lty = 1)

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Explanation:

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Example 2 Using lines():

Another way to plot multiple lines is to plot them one by one using the points() and lines() functions. Here’s an example:

# Create sample data
x <- 1:10
y1 <- c(1, 4, 3, 6, 5, 8, 7, 9, 10, 2)
y2 <- c(2, 5, 4, 7, 6, 9, 8, 10, 3, 1)
y3 <- c(3, 6, 5, 8, 7, 10, 9, 2, 4, 1)

# Create an empty plot
plot(x, y1, type = "n", xlim = c(1, 10), ylim = c(0, 10), 
     xlab = "X", ylab = "Y", main = "Multiple Lines Plot")

# Plot each line one by one
lines(x, y1, type = "l", col = "red")
lines(x, y2, type = "l", col = "blue")
lines(x, y3, type = "l", col = "green")

# Add a legend
legend("topright", legend = c("Line 1", "Line 2", "Line 3"), 
       col = c("red", "blue", "green"), lty = 1)

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Explanation

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Conclusion

In this blog post, we explored two methods for plotting multiple lines on a graph using base R: matplot() and lines(). We provided step-by-step examples and explained the code in simple terms. We encourage you to try these methods on your own datasets and experiment with different customization options. By mastering these techniques, you will be able to create visually appealing and informative plots in R. Happy plotting!

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References

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